Anomaly detection system

ABSTRACT

An image analysis system including an image gathering unit that gathers a high-altitude image having multiple channels, an image analysis unit that segments the high-altitude image into a plurality of equally size tiles and determines an index value based on at least one channel of the image where the image analysis unit identifies areas containing anomalies in each image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/245,758 filed Jan. 11, 2019, which is a non-provisional patentapplication that claims the benefit of and the priority from U.S.Provisional Patent Application No. 62/616,159, filed Jan. 11, 2018,titled ANOMALY DETECTION SYSTEM.

BACKGROUND OF THE INVENTION

The agriculture industry comprises a large portion of the world'seconomy. In addition, as the population of the world increases annually,more food must be produced by existing agricultural assets. In order toincrease yields on existing plots of farm land, producers require aclear understanding of plant and soil conditions. However, as a singlefarm may encompass hundreds of acres, it is difficult to access theconditions of the farm land.

Currently, farmers rely on their observations of their land along withprior experience to determine the requirements to increase the yield oftheir farm land. These observations may include identifying locations ofweeds, identifying plant illnesses and determining levels of cropdamage. However, considering the large number of acres in the averagefarm, these observations are not a reliable method to increase yields.Therefore, a need exists for system that will allow a farmer to betterunderstand the conditions of their farm land.

SUMMARY OF THE INVENTION

Systems, methods, features, and advantages of the present invention willbe or will become apparent to one with skill in the art upon examinationof the following figures and detailed description. It is intended thatall such additional systems, methods, features, and advantages beincluded within this description, be within the scope of the invention,and be protected by the accompanying claims.

One embodiment of the present disclosure includes an image analysissystem including an image gathering unit that gathers a high-altitudeimage having multiple channels, an image analysis unit that segments thehigh-altitude image into a plurality of equally size tiles anddetermines an index value based on at least one channel of the imagewhere the image analysis unit identifies areas containing anomalies ineach image.

In another embodiment, the index determined is a normal differentialvegetation index for a segment of the captured image.

In another embodiment, the index determined is a soil adjustedvegetation index for a segment of the captured image.

In another embodiment, the image analysis unit masks the segment of theimage using a confidence mask based on the index value.

In another embodiment, the image analysis unit normalizes the maskedsegment of the image.

In another embodiment, the image analysis unit calculates a mean andstandard deviation of the segment of the normalized image.

In another embodiment, the image analysis unit applies a box averagingthreshold to the segment of the normalized image.

In another embodiment, the image analysis unit calculates a mean foreach pixel in the applied box.

In another embodiment, the image analysis unit removes pixels from thesegment of the image that have a calculated mean below a predeterminedthreshold.

In another embodiment, the image analysis unit calculates a score foreach of the remaining pixels and draws a rectangle around groups ofpixels based on the scores of each pixel.

Another embodiment of the present disclosure includes, a method ofanalyzing an image, the method including the steps of gathering ahigh-altitude image having multiple channels via an image gatheringunit, segmenting the high-altitude image into a plurality of equallysize tiles via an image analysis unit, determining an index value basedon at least one channel of the image via the image analysis unit,identifying areas containing anomalies in each image via the imageanalysis unit.

In another embodiment, the index determined is a normal differentialvegetation index for a segment of the captured image.

In another embodiment, the index determined is a soil adjustedvegetation index for a segment of the captured image.

In another embodiment, the step of identifying anomalies includesmasking the segment of the image using a confidence mask based on theindex value.

In another embodiment, the step of identifying anomalies includesnormalizing the masked segment of the image.

In another embodiment, the step of identifying anomalies includescalculating a mean and standard deviation of the segment of thenormalized image.

In another embodiment, the step of identifying anomalies includesapplying a box averaging threshold to the segment of the normalizedimage.

In another embodiment, the step of identifying anomalies includescalculating a mean for each pixel in the applied box.

In another embodiment, the step of identifying anomalies includesremoving pixels from the segment of the image that have a calculatedmean below a predetermined threshold.

In another embodiment, the step of identifying anomalies includescalculating a score for each of the remaining pixels and drawing arectangle around groups of pixels based on the scores of each pixel.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an implementation of the presentinvention and, together with the description, serve to explain theadvantages and principles of the invention. In the drawings:

FIG. 1 depicts one embodiment of an anomaly identification systemconsistent with the present invention;

FIG. 2 depicts one embodiment of an anomaly detection unit;

FIG. 3 depicts one embodiment of a communication device consistent withthe present invention;

FIG. 4 depicts a schematic representation of a process used to calculateidentify anomalies in an image;

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings which depict different embodimentsconsistent with the present invention, wherever possible, the samereference numbers will be used throughout the drawings and the followingdescription to refer to the same or like parts.

The anomaly identification system 100 gathers medium to low resolutionimages gathered from an aircraft flying above 1,500 feet. Each image isthen analyzed using NDVI and SDVI parameters and is normalized. Afternormalization, specific adjacent areas are analyzed to identityanomalies in each portion. After specific anomalies are identified, thesystem combines the various portions to generate a map of all anomaliesit the image that are identified using rectangles outlining the anomalyarea.

FIG. 1 depicts one embodiment of an anomaly identification system 100consistent with the present invention. The anomaly identification system100 includes a anomaly identification device 102, a communication device1 104, a communication device 2 106 each communicatively connected via anetwork 108. The anomaly identification system 100 further includes animage gathering unit 110, an image analysis unit 112, a preprocessingunit 114 and an anomaly identification unit 116.

The image gathering unit 110 and image analysis unit 112 may be embodiedby one or more servers. Alternatively, each of the preprocessing unit114 and anomaly identification unit 116 may be implemented using anycombination of hardware and software, whether as incorporated in asingle device or as a functionally distributed across multiple platformsand devices.

In one embodiment, the network 108 is a cellular network, a TCP/IPnetwork, or any other suitable network topology. In another embodiment,the anomlay identification device may be servers, workstations, networkappliances or any other suitable data storage devices. In anotherembodiment, the communication devices 104 and 106 may be any combinationof cellular phones, telephones, personal data assistants, or any othersuitable communication devices. In one embodiment, the network 102 maybe any private or public communication network known to one skilled inthe art such as a local area network (“LAN”), wide area network (“WAN”),peer-to-peer network, cellular network or any suitable network, usingstandard communication protocols. The network 108 may include hardwiredas well as wireless branches. The image gathering unit 112 may be adigital camera.

FIG. 2 depicts one embodiment of an anomaly detection device 102. Theanomaly detection device 102 includes a network I/O device 204, aprocessor 202, a display 206 and a secondary storage 208 running imagestorage unit 210 and a memory 212 running a graphical user interface214. The image gathering unit 112, operating in memory 208 of theanomaly detection unit 102, is operatively configured to receive animage from the network I/O device 204. In one embodiment, the processor202 may be a central processing unit (“CPU”), an application specificintegrated circuit (“ASIC”), a microprocessor or any other suitableprocessing device. The memory 212 may include a hard disk, random accessmemory, cache, removable media drive, mass storage or configurationsuitable as storage for data, instructions, and information. In oneembodiment, the memory 208 and processor 202 may be integrated. Thememory may use any type of volatile or non-volatile storage techniquesand mediums. The network I/O line 204 device may be a network interfacecard, a cellular interface card, a plain old telephone service (“POTS”)interface card, an ASCII interface card, or any other suitable networkinterface device. The anomaly detection unit 114 may be a compiledprogram running on a server, a process running on a microprocessor orany other suitable port control software.

FIG. 3 depicts one embodiment of a communication device 104/106consistent with the present invention. The communication device 104/1106includes a processor 302, a network I/O Unit 304, an image capture unit306, a secondary storage unit 308 including an image storage device 310,and memory 312 running a graphical user interface 314. In oneembodiment, the processor 302 may be a central processing unit (“CPU”),a application specific integrated circuit (“ASIC”), a microprocessor orany other suitable processing device. The memory 312 may include a harddisk, random access memory, cache, removable media drive, mass storageor configuration suitable as storage for data, instructions, andinformation. In one embodiment, the memory 312 and processor 302 may beintegrated. The memory may use any type of volatile or non-volatilestorage techniques and mediums. The network I/O device 304 may be anetwork interface card, a plain old telephone service (“POTS”) interfacecard, an ASCII interface card, or any other suitable network interfacedevice.

In one embodiment, the network 108 may be any private or publiccommunication network known to one skilled in the art such as a LocalArea Network (“LAN”), Wide Area Network (“WAN”), Peer-to-Peer Network,Cellular network or any suitable network, using standard communicationprotocols. The network 108 may include hardwired as well as wirelessbranches.

FIG. 4 depicts a schematic representation of a process used to identifyanomalies in an image. In step 402, an image is captured by the imagegathering unit 112. The image may be captured using any conventionalmethods of capturing a digital image. In one embodiment, the image is ahigh resolution raw image. In one embodiment, the image is captured froman aircraft flying at least 1,500 feet above the surface. In step 404,the Normal Differential Vegetation Index (NDVI) or the Soil AdjustedVegetation Index. The NDVI is calculated using the following equation:

${N\; D\; V\; I} = \frac{{NIR} - {RED}}{{NIR} + {RED}}$

The near field channel (NIR) is identified in the image by the imageanalysis unit 114 where the near field channel is between 800 nm and 850nm. The red channel (RED) is identified in each image by the imageanalysis unit 114 where the red channel is between 650 nm and 680 nm.The SDVI is determined using the following equation:

${S\; A\; V\; I} = \frac{{NIR} - {RED}}{( {{NIR} + {RED} + L} )( {1 + L} )}$

Where L=0.5.

In step 406, the image analysis unit 114 masks the image using aconfidence mask based on the NDVI or SDVI calculation. In step 408, theimage is normalized. In one embodiment, a Gaussian distribution of imagepixel values is performed by the image analysis unit 114. After theGaussian distribution is applied, outlying pixels are identified aspotential anomalies. The image is normalized using the followingequation:

${{normalized}\mspace{11mu}( {x,y} )} = \frac{{{image}\mspace{11mu}( {x,y} )} - {\mu( {x,y} )}}{\sigma( {x,y} )}$where x is a row of pixels, y is a column of pixels, and μ(x,y) andσ(x,y) are the mean and standard deviation values. In step 410, theimage analysis unit 114 calculates the mean and standard deviationvalues based on the portion of the image being analyzed. For globalimages, the mean and standard deviation are calculated taking intoaccount all pixels in the image. For local images, i.e. the pixel (x,y),the mean and the standard deviation are determined taking into accountonly the square area around the pixel where the square area is aconstant predetermined size.

In step 412, the image analysis unit 114 applies a box averagingthreshold to prospective anomalies identified in the image. In oneembodiment, a square box of a predetermined size is positioned around aspecific anomaly area. In one embodiment, the box is 50 pixels by 50pixels. The pixels in the box are scanned one pixel at a time and themean value of the pixels in the box is calculated. If the mean value isgreater than a predetermined threshold, the box is marked as an anomaly.In step 414, small areas marked as anomalies, areas less than 0.1% ofthe entire image, are removed as marked anomalies. In step 416, areasconnected to the identified anomaly areas to determine the portions ineach area that represent an anomaly. In step 418, cover rectangles arepositioned around each anomaly. In step 420, the image analysis unit 114calculates a score for each identified anomaly. In step 422, the anomalyrectangles are overlaid on the image as a whole to identify the anomalyareas.

What is claimed:
 1. An image analysis system including: an image captureunit that captures at least one a high-altitude image having multiplechannels; an image analysis unit operating in the memory of a computerthat segments the high-altitude image into a plurality of tiles witheach tile having a same pixel width and a same pixel height as anadjacent tile and determines an index value based on at least onechannel of the image, wherein the image analysis unit performs a pixelby pixel analysis of the pixels in each tile to calculate a mean valuefor each tile, the image analysis unit identifies areas containinganomalies in each image by analyzing the mean value for each areaconnected to identified anomalies to determine areas where anomaliesexist, and a score is assigned to each identified anomaly and an anomalyrectangle is placed on the image to identify areas where anomalies areidentified.
 2. The image analysis system of claim 1, wherein the indexdetermined is a normal differential vegetation index for a segment ofthe captured image.
 3. The image analysis system of claim 1, wherein theindex determined is a soil adjusted vegetation index for a segment ofthe captured image.
 4. The image analysis system of claim 1, wherein theimage analysis unit masks the segment of the image using a confidencemask based on the index value.
 5. The image analysis system of claim 4,wherein the image analysis unit normalizes the masked segment of theimage.
 6. The image analysis system of claim 5, wherein the imageanalysis unit calculates a mean and standard deviation of the segment ofthe normalized image.
 7. The image analysis system of claim 6, whereinthe image analysis unit applies a box averaging threshold to the segmentof the normalized image.
 8. The image analysis system of claim 7 whereinthe image analysis unit calculates a mean for each pixel in the appliedbox.
 9. The image analysis system of claim 8, wherein the image analysisunit removes pixels from the segment of the image that have a calculatedmean below a predetermined threshold.
 10. The image analysis system ofclaim 9, wherein the image analysis unit calculates a score for each ofthe remaining pixels and draws a rectangle around groups of pixels basedon the scores of each pixel.
 11. An image analysis unit including aprocessor and a memory with a method of analyzing an image performed inthe memory, the method including the steps of: gathering a high-altitudeimage having multiple channels via an image capture unit; segmenting thehigh-altitude image into a plurality of tiles with each tile having asame pixel width and a same pixel height as an adjacent tile via animage analysis unit; determining an index value based on at least onechannel of the image via the image analysis unit; performing a pixel bypixel analysis of the pixels in each tile to calculate a mean value foreach tile via the image analysis unit, identifying areas containinganomalies in each image via the image analysis unit identifies areascontaining anomalies in each image by analyzing the mean value for eacharea connected to identified anomalies to determine areas whereanomalies exist, and assigning a score to each identified anomaly andplacing an anomaly rectangle on the image to identify areas whereanomalies are identified.
 12. The method of claim 11, wherein the indexdetermined is a normal differential vegetation index for a segment ofthe captured image.
 13. The image analysis system of claim 11, whereinthe index determined is a soil adjusted vegetation index for a segmentof the captured image.
 14. The image analysis system of claim 11,wherein the step of identifying anomalies includes masking the segmentof the image using a confidence mask based on the index value.
 15. Theimage analysis system of claim 14, wherein the step of identifyinganomalies includes normalizing the masked segment of the image.
 16. Theimage analysis system of claim 15, wherein the step of identifyinganomalies includes calculating a mean and standard deviation of thesegment of the normalized image.
 17. The image analysis system of claim16, wherein the step of identifying anomalies includes applying a boxaveraging threshold to the segment of the normalized image.
 18. Theimage analysis system of claim 17, wherein the step of identifyinganomalies includes calculating a mean for each pixel in the applied box.19. The image analysis system of claim 18, wherein the step ofidentifying anomalies includes removing pixels from the segment of theimage that have a calculated mean below a predetermined threshold. 20.The image analysis system of claim 19, wherein the step of identifyinganomalies includes calculating a score for each of the remaining pixelsand drawing a rectangle around groups of pixels based on the scores ofeach pixel.